"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
_A        =     logging.get_logger(__name__)




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase=False							)				-> Tuple:
				SCREAMING_SNAKE_CASE__			=					[]
				for i in range(config.num_hidden_layers							):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""")							)
								rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""")							)
								rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""")							)
								rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""")							)
								rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""")							)
								rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""")							)
								rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""")							)
								rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""")							)
								rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""")							)
								rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""")							)

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        ("cls_token", "vit.embeddings.cls_token"),
				        ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
				        ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
				        ("pos_embed", "vit.embeddings.position_embeddings"),
				    ]							)

				if base_model:
								# layernorm + pooler
								rename_keys.extend(
								    [
								        ("norm.weight", "layernorm.weight"),
								        ("norm.bias", "layernorm.bias"),
								        ("pre_logits.fc.weight", "pooler.dense.weight"),
								        ("pre_logits.fc.bias", "pooler.dense.bias"),
								    ]							)

								# if just the base model, we should remove "vit" from all keys that start with "vit"
								SCREAMING_SNAKE_CASE__			=					[(pair[0], pair[1][4:]) if pair[1].startswith("vit"							) else pair for pair in rename_keys]
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ("norm.weight", "vit.layernorm.weight"),
								        ("norm.bias", "vit.layernorm.bias"),
								        ("head.weight", "classifier.weight"),
								        ("head.bias", "classifier.bias"),
								    ]							)

				return rename_keys




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase	,       __UpperCAmelCase=False							)				-> str:
				for i in range(config.num_hidden_layers							):
								if base_model:
												SCREAMING_SNAKE_CASE__			=					''''''
								else:
												SCREAMING_SNAKE_CASE__			=					'''vit.'''
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								SCREAMING_SNAKE_CASE__			=					state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""							)
								SCREAMING_SNAKE_CASE__			=					state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""							)
								# next, add query, keys and values (in that order) to the state dict
								SCREAMING_SNAKE_CASE__			=					in_proj_weight[
								    : config.hidden_size, :
								]
								SCREAMING_SNAKE_CASE__			=					in_proj_bias[: config.hidden_size]
								SCREAMING_SNAKE_CASE__			=					in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								SCREAMING_SNAKE_CASE__			=					in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								SCREAMING_SNAKE_CASE__			=					in_proj_weight[
								    -config.hidden_size :, :
								]
								SCREAMING_SNAKE_CASE__			=					in_proj_bias[-config.hidden_size :]




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase							)				-> Union[str, Any]:
				SCREAMING_SNAKE_CASE__			=					['''head.weight''', '''head.bias''']
				for k in ignore_keys:
								state_dict.pop(lowerCamelCase_	,       lowerCamelCase_							)




def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase	,       __UpperCAmelCase							)				-> List[str]:
				SCREAMING_SNAKE_CASE__			=					dct.pop(lowerCamelCase_							)
				SCREAMING_SNAKE_CASE__			=					val




def     SCREAMING_SNAKE_CASE       (	)				-> List[str]:
				SCREAMING_SNAKE_CASE__			=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				SCREAMING_SNAKE_CASE__			=					Image.open(requests.get(lowerCamelCase_	,       stream=lowerCamelCase_							).raw							)
				return im




@torch.no_grad()
def     SCREAMING_SNAKE_CASE       (	__UpperCAmelCase	,       __UpperCAmelCase							)				-> List[str]:
				SCREAMING_SNAKE_CASE__			=					ViTConfig()
				SCREAMING_SNAKE_CASE__			=					False
				# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
				if vit_name[-5:] == "in21k":
								SCREAMING_SNAKE_CASE__			=					True
								SCREAMING_SNAKE_CASE__			=					int(vit_name[-12:-10]							)
								SCREAMING_SNAKE_CASE__			=					int(vit_name[-9:-6]							)
				else:
								SCREAMING_SNAKE_CASE__			=					1_000
								SCREAMING_SNAKE_CASE__			=					'''huggingface/label-files'''
								SCREAMING_SNAKE_CASE__			=					'''imagenet-1k-id2label.json'''
								SCREAMING_SNAKE_CASE__			=					json.load(open(hf_hub_download(lowerCamelCase_	,       lowerCamelCase_	,       repo_type="dataset"							)	,       "r"							)							)
								SCREAMING_SNAKE_CASE__			=					{int(lowerCamelCase_							): v for k, v in idalabel.items()}
								SCREAMING_SNAKE_CASE__			=					idalabel
								SCREAMING_SNAKE_CASE__			=					{v: k for k, v in idalabel.items()}
								SCREAMING_SNAKE_CASE__			=					int(vit_name[-6:-4]							)
								SCREAMING_SNAKE_CASE__			=					int(vit_name[-3:]							)
				# size of the architecture
				if "deit" in vit_name:
								if vit_name[9:].startswith("tiny"							):
												SCREAMING_SNAKE_CASE__			=					192
												SCREAMING_SNAKE_CASE__			=					768
												SCREAMING_SNAKE_CASE__			=					12
												SCREAMING_SNAKE_CASE__			=					3
								elif vit_name[9:].startswith("small"							):
												SCREAMING_SNAKE_CASE__			=					384
												SCREAMING_SNAKE_CASE__			=					1_536
												SCREAMING_SNAKE_CASE__			=					12
												SCREAMING_SNAKE_CASE__			=					6
								else:
												pass
				else:
								if vit_name[4:].startswith("small"							):
												SCREAMING_SNAKE_CASE__			=					768
												SCREAMING_SNAKE_CASE__			=					2_304
												SCREAMING_SNAKE_CASE__			=					8
												SCREAMING_SNAKE_CASE__			=					8
								elif vit_name[4:].startswith("base"							):
												pass
								elif vit_name[4:].startswith("large"							):
												SCREAMING_SNAKE_CASE__			=					1_024
												SCREAMING_SNAKE_CASE__			=					4_096
												SCREAMING_SNAKE_CASE__			=					24
												SCREAMING_SNAKE_CASE__			=					16
								elif vit_name[4:].startswith("huge"							):
												SCREAMING_SNAKE_CASE__			=					1_280
												SCREAMING_SNAKE_CASE__			=					5_120
												SCREAMING_SNAKE_CASE__			=					32
												SCREAMING_SNAKE_CASE__			=					16

    # load original model from timm
				SCREAMING_SNAKE_CASE__			=					timm.create_model(lowerCamelCase_	,       pretrained=lowerCamelCase_							)
				timm_model.eval()

				# load state_dict of original model, remove and rename some keys
				SCREAMING_SNAKE_CASE__			=					timm_model.state_dict()
				if base_model:
								remove_classification_head_(lowerCamelCase_							)
				SCREAMING_SNAKE_CASE__			=					create_rename_keys(lowerCamelCase_	,       lowerCamelCase_							)
				for src, dest in rename_keys:
								rename_key(lowerCamelCase_	,       lowerCamelCase_	,       lowerCamelCase_							)
				read_in_q_k_v(lowerCamelCase_	,       lowerCamelCase_	,       lowerCamelCase_							)

				# load HuggingFace model
				if vit_name[-5:] == "in21k":
								SCREAMING_SNAKE_CASE__			=					ViTModel(lowerCamelCase_							).eval()
				else:
								SCREAMING_SNAKE_CASE__			=					ViTForImageClassification(lowerCamelCase_							).eval()
				model.load_state_dict(lowerCamelCase_							)

				# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
				if "deit" in vit_name:
								SCREAMING_SNAKE_CASE__			=					DeiTImageProcessor(size=config.image_size							)
				else:
								SCREAMING_SNAKE_CASE__			=					ViTImageProcessor(size=config.image_size							)
				SCREAMING_SNAKE_CASE__			=					image_processor(images=prepare_img()	,       return_tensors="pt"							)
				SCREAMING_SNAKE_CASE__			=					encoding['''pixel_values''']
				SCREAMING_SNAKE_CASE__			=					model(lowerCamelCase_							)

				if base_model:
								SCREAMING_SNAKE_CASE__			=					timm_model.forward_features(lowerCamelCase_							)
								assert timm_pooled_output.shape == outputs.pooler_output.shape
								assert torch.allclose(lowerCamelCase_	,       outputs.pooler_output	,       atol=1e-3							)
				else:
								SCREAMING_SNAKE_CASE__			=					timm_model(lowerCamelCase_							)
								assert timm_logits.shape == outputs.logits.shape
								assert torch.allclose(lowerCamelCase_	,       outputs.logits	,       atol=1e-3							)

				Path(lowerCamelCase_							).mkdir(exist_ok=lowerCamelCase_							)
				print(F"""Saving model {vit_name} to {pytorch_dump_folder_path}"""							)
				model.save_pretrained(lowerCamelCase_							)
				print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
				image_processor.save_pretrained(lowerCamelCase_							)


if __name__ == "__main__":
						_A        =     argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--vit_name',
						    default='vit_base_patch16_224',
						    type=str,
						    help='Name of the ViT timm model you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
						)

						_A        =     parser.parse_args()
						convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)







from ..utils import DummyObject, requires_backends







class 					__lowercase  (						metaclass=_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		Optional[Any]								=	['''speech''']




							def __init__(					self    ,						*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       Dict:
														requires_backends(self    ,						['''speech''']						)







class 					__lowercase  (						metaclass=_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		int								=	['''speech''']




							def __init__(					self    ,						*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       Tuple:
														requires_backends(self    ,						['''speech''']						)






